Training-time (a.k.a. poisoning or input manipulation) attacks were developed against various machine learning algorithms from deep networks ([1]}) to support vector machines ([2]}) to linear regression models ([3]}, [4]}) to collaborative filtering ([5]}, [6]}) and online centroid anomaly detection ([7]}).
We complement this by furthering knowledge of another canonical learning method.
We believe we are the first to investigate (theoretically or otherwise) training-time attacks against \(k\) NN.
